Statistical Machine Learning 1RT700 61808 VT2025

Course content

This is an introductory course to statistical machine learning for students with some background in elementary calculus, linear algebra, and probability theory.

The course is focusing on supervised learning, i.e., classification and regression with real data, and its computational and statistical foundations. In addition to the necessary theory (e.g., derivations and proofs), this course also includes practical sessions (notably the lab and the project). The practical part will be implemented using Python.

The recommended course book is Machine Learning - A First Course for Engineers and Scientists, which is available online free of charge. Certain chapters will be recommended for reading each week. Link to videos and other material are also provided below.

Prerequisite knowledge

Basic probability theory:

The course assumes the students have background in elementary calculus, linear algebra, and probability theory.

The following examples are concepts used in the course and it is expected that the students have some familiarity with them:

Python introduction:

The course assumes some programming knowledge, and we will use Python throughout the course. Have a look at the following notebook if you need a Python refresher. The notebook provides an introduction to NumPy, Pandas and Matplotlib, and the exercises include explainations that may help you in the first computer class. The notebook and suggested solutions are found here: Notebook, Solutions, Open in colab

We also recomment the "Introduction to Python" crash course linked on the lecture page.

Course structure

Click on the links above to get further information about each course element.

Course evaluation

This year's course evaluation can be found here, and our teacher report here. See also the course evaluation with the corresponding teacher report from the previous course instance (VT2024).

Schedule

The schedule is available in TimeEdit.

Exercise sessions

During the exercise sessions, the teaching assistant will give a short introduction of the material related to that session and during the session provide insight and comments on the solutions. Except for this, you are supposed to be active and work with the suggested exercises and the teaching assistants will be available for questions and discussion. Take this opportunity to interact! The exercises will be available ahead of time before the session. There are three time slots for each exercise session in the schedule. Choose the one that fits best with your schedule.

Grading

A written final exam is scheduled for March 22th. To pass the course you need to pass the project, lab, and exam.

Policy on generative AI

In this course, you may use generative AI as a tool to facilitate learning and to seek knowledge and deepening within the course. It is also permitted to use generative AI to produce material that is included in the answers to examination tasks, provided that all use is clearly reported. Remember that you are fully responsible for the content of everything you submit, and must be able to stand by it and, if necessary, argue for it. An exception is made for AI tools for language checking. The use of such tools to only check spelling and pure grammatical errors in an existing text does not need to be reported.

 

Teachers

Course responsible Jens Sjölund
Lecturer Sara Hamis
Lecturer Jodie Cochrane
Lecturer Antonio Horta Ribeiro
Teaching Assistant Jennifer Rosina Andersson
Teaching Assistant Ziwei Luo
Teaching Assistant Daniel Hernández Escobar
Teaching Assistant Jingwei Hu
Teaching Assistant Robin Hollifeldt
Teaching Assistant Ella Schmidtobreick
Teaching Assistant Anton O-Nils
Teaching Assistant Erik Mandel